• Journal of Labour Economics
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• v.33 no.1
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• pp.53-84
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• 2010

This paper investigates the wage losses of displaced workers, using data from the Korean Labor & Income Panel Study. Previous Korean studies were only to estimate the wage losses shortly after job displacement. However, if the wage losses persist, those results seem to under-estimate the effects of displacement. Our findings show that the wage reductions are quite persistent 4 years after displacement. And much of this persistence can be explained by the lost labor market experience (lost opportunities to accumulate generalized skills) and additional job losses in the years following an initial displacement.

• Journal of information and communication convergence engineering
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• v.11 no.4
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• pp.235-240
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• 2013

Cloud systems are popular computing environment because they can provide easy access to computing resources for users as well as efficient use of resources for companies. The resources of cloud computing are heterogeneous and jobs have various characteristics. One such issue is effective job scheduling. Scheduling in the cloud system may be defined as a multiple criteria decision model. To address this issue, this paper proposes a priority-based two-step job scheduling algorithm. On the first level, jobs are classified based on preference. Resources are dedicated to a job if a deadline failure would cause severe results or critical business losses. In case of only minor discomfort or slight functional impairment, the job is scheduled using a best effort approach. On the second level, jobs are allocated to adequate resources through their priorities that are calculated by the analytic hierarchic process model. We then analyze the proposed algorithm and make a scheduling example to confirm its efficiency.

• 한국사회정책
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• v.24 no.4
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• pp.113-150
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• 2017

This study analyzes how workers become impoverished and have their jobs less stabilized after they suffer from non-job-related sickness. Given that South Korea lacks sickness benefits, which most of OECD member states legislate and implement except US and Switzerland, this study examines its impact on laborers' job stability and povertization in Korea. The researchers have conducted in-depth interviews with nine former or present laborers who have the experiences and four experts on the issue in July-September, 2017 for the qualitative analysis. It is found that laborers, after becoming aware of their sickness, at first endure their pain without informing their employers not to lose their jobs. The attititude is observed especially among non-standard laborers, because sickness more often leads to job loss for them than for standard laborers. After workers have to leave their jobs due to their sickness in the end, they have no choice but to keep working in less stable jobs to compensate for income losses. They become gradually impoverished with their social capital like family bond declining. We observe laborers who are eligible for industrial accident insurance compensation could not benefit from the system because some employers refrain from the legal reporting duty. Due to this illegal practices, some industrial accident victims unduly lose their jobs due to "non-job-related sickness". Second, some employers report to the authority that their sick laborers have left their jobs 'voluntarily' even when they have quitted it without their volition, in which case the newly unemployed are not eligible for unemployment benefits. Large holes in Korea's safety nets for those suffering from multiple risks of sickness and unemployment.

• The Journal of the Korea Contents Association
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• v.22 no.7
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• pp.81-88
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• 2022

This study is to investigate the perception of domestic appraisers about the possibility of using artificial intelligence (AI) and related risks from the use of AI in the appraisal industry. We conducted a mobile survey of evaluators from February 10 to 18, 2022. We collected survey data from 193 respondents. Frequency analysis and multiple response analysis were performed for basic analysis. When AI is used in the appraisal industry, factor analysis was used to analyze various types of risks. Although appraisers have a positive perception of AI introduction in the appraisal industry, they considered collateral, consulting, and taxation, mainly in areas where AI is likely to be used and replaced, mainly negative effects related to job losses and job replacement. They were more aware of the alternative risks caused by AI in the field of human labor. I was very aware of responsibilities, privacy and security, and the risk of technical errors. However, fairness, transparency, and reliability risks were generally perceived as low risk issues. Existing studies have mainly studied analysis methods that apply AI to mass evaluation models, but this study focused on the use and risk of AI. Understanding industry experts' perceptions of AI utilization will help minimize potential risks when AI is introduced on a large scale.